This [two-armed bandit] problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions.

In this study, the researchers examined the decision-making ability of slime mold using a test classically used in humans, birds and other brained organisms: the two-armed bandit problem, named for the infamous slot machine, or one-armed bandit. In a two-armed bandit problem, the subject has two levers to pull, each of which delivers a certain, randomly determined reward. One of the levers is more likely to deliver a higher reward overall, so the challenge for participants is to decide at what point to stop exploring both options and decide to exclusively exploit just the one option in order to maximize their payoff. The phenomenon is called the exploration-exploitation tradeoff and is relevant to more than just slot machines, applying to many situations, including investors picking start-up companies to back or drivers choosing a parking space. As such, it has become a classical tool for testing the decision-making abilities of humans and other animals, but it has never before been used on an organism without a brain.

The researchers adapted the two-armed bandit test for slime mold by giving the organism the choice to explore two opposite directions. In each direction, the slime mold encountered discrete patches of food, more or less regularly distributed. One direction would contain more of these patches than the other. They then observed how far in each direction the slime mold would explore before switching to the exploitation of one of the two directions only. The results of these experiments demonstrate that slime mold compares the relative qualities of multiple options, most often choosing the direction with the higher overall concentration of food. It was able to sum up the number of food patches encountered in each direction, as well as the quantity of food present at each patch to make correct and adaptive decisions as to the direction it should move next.